Patient decision aids for aortic stenosis and chronic coronary artery disease: a systematic review and meta-analysis
Bibliographic record
Abstract
AIMS: Shared decision-making is recommended for patients considering treatment options for severe aortic stenosis (AS) and chronic coronary artery disease (CAD). This review aims to systematically identify and assess patient decision aids (PtDAs) for chronic CAD and AS and evaluate the international evidence on their effectiveness for improving the quality of decision-making. METHODS AND RESULTS: Five databases (Cochrane, CINAHL, Embase, MEDLINE, and PsycInfo), clinical trial registers, and 30 PtDA repositories/websites were searched from 2006 to March 2023. Screening, data extraction, and quality assessments were completed independently by multiple reviewers. Meta-analyses were conducted using Stata statistical software. Eleven AS and 10 CAD PtDAs were identified; seven were less than 5 years old. Over half of the PtDAs were web based and the remainder paper based. One AS and two CAD PtDAs fully/partially achieved international PtDA quality criteria. Ten studies were included in the review; four reported on the development/evaluation of AS PtDAs and six on CAD PtDAs. Most studies were conducted in the USA with White, well-educated, English-speaking participants. No studies fulfilled all quality criteria for reporting PtDA development and evaluation. Meta-analyses found that PtDAs significantly increased patient knowledge compared with 'usual care' (mean difference: 0.620; 95% confidence interval 0.396-0.845, P < 0.001) but did not change decisional conflict. CONCLUSION: Patients who use PtDAs when considering treatments for AS or chronic CAD are likely to be better informed than those who do not. Existing PtDAs may not meet the needs of people with low health literacy levels as they are rarely involved in their development. REGISTRATION: PROSPERO: CRD42021264700.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".